Graph Embedding-Based Virtual Network Mapping Method

ABSTRACT

The present invention provides a graph embedding-based virtual network mapping method including inputting a virtual network and a substrate network corresponding to a virtual network mapping scenario, in an optimized mapping mode, generating an embedding value for every substrate node by applying a graph convolution network (GCN), by a network encoder, to embed the virtual network, determining whether a difference between an embedding value for every substrate node and an embedding value for every previous substrate node which is previously embedded exceeds a set threshold, by a network decoder, and mapping an allocation node of the virtual network to a mapping node of a previous substrate network which is previously mapped according to an embedding value for every previous substrate node when the difference value does not exceed the threshold value, by the network decoder.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the priority of Korean Patent Application No.10-2021-017367810 filed on Nov. 29, 2021, in the Korean IntellectualProperty Office, the disclosure of which is incorporated herein byreference.

TECHNICAL FIELD

The present invention relates to a graph embedding-based virtual networkmapping method, and more specifically, to a graph embedding-basedvirtual network mapping method which easily maps a virtual network to asubstrate network by graph embedding regardless of an operationenvironment, such as a network size and a VNM scenario.

BACKGROUND ART OF INVENTION

Network virtualization is a network technology which accommodatesvarious types of user groups without causing mutual interference using asingle physical network resource.

At this time, in the network virtualization, virtual network mapping(VNM) is a function of allocating a virtual network formed by disposingnodes having a physical network resource to a substrate network which isa physical network, for example, data center nodes and internet serviceprovider nodes.

A virtual network is mapped or allocated to the substrate networkthrough the VNM to operate over a substrate network, and this problem ishandled in a data center network, an internet service provider, cloudcomputing, and IoT.

At this time, the virtual network mapping is performed by embedding thesubstrate network and the virtual network expressed as an image formusing the CNN, or embeds the substrate network and the virtual networkusing a general multi-layer perceptron (MLP).

Here, there are problems in that the above-described virtual networkmapping is focused on embedding, and a structure of the reinforcementlearning needs to be changed in an environment with a dynamic networksize.

Recently, a method for embedding the virtual network and the substratenetwork regardless of the operation environment is being studied.

DISCLOSURE OF INVENTION Technical Problem to be Solved

An object of the present invention is to provide a graph embedding-basedvirtual network mapping method which easily maps a virtual network to asubstrate network by graph embedding, regardless of an operationenvironment, such as a network size and a VNM scenario.

The objects of the present invention are not limited to theabove-mentioned objects, and other objects and advantages of the presentinvention which have not been mentioned above can be understood by thefollowing description and become more apparent from exemplaryembodiments of the present invention. Further, it may be understood thatthe objects and advantages of the present invention may be embodied bythe means and a combination thereof in the claims.

According to an aspect of the present invention, a graph embedding-basedvirtual network mapping method may include: inputting a virtual networkand a substrate network corresponding to a virtual network mappingscenario; in an optimized mapping mode, generating an embedding valuefor every substrate node by applying a graph convolution network (GCN),by a network encoder, to embed the virtual network; determining whethera difference value between an embedding value for every substrate nodeand an embedding value for every previous substrate node which ispreviously embedded exceeds a set threshold, by a network decoder; andmapping an allocation node of the virtual network to a mapping node of aprevious substrate network which is previously mapped according to anembedding value for every previous substrate node when the differencevalue does not exceed the threshold value, by the network decoder.

Technical Solution to Solve Problems

The method may further include controlling the network encoder togenerate an embedding value for every node by embedding the substratenetwork by the network decoder when the difference exceeds thethreshold.

In the mapping, an allocation node of the virtual network and a mappingnode of the substrate network may be mapped to each other based on theembedding value for every substrate node and the embedding value forevery node.

In the mapping, the allocation node having a highest embedding value isselected among the embedding values for every substrate node, and theallocation node and the mapping node may be mapped.

The method may further include: generating an embedding value for everynode by embedding the embedding value for every substrate node and thesubstrate network by the network encoder, in a normal mapping mode; andmapping an allocation node of the virtual network and a mapping node ofthe substrate network based on the embedding value for every substratenode and the embedding value for every node, by the network decoder.

The method further may further include determining whether a currentmode is an optimized mapping mode before the generating of the embeddingvalue.

In the generating of an embedding value, the embedding value for everysubstrate node and the embedding value for every node may be generatedby applying a distance between nodes of the virtual network and adistance between nodes of the substrate network to the MLP function.

Advantageous Effects of Invention

A graph embedding-based virtual network mapping method according to thepresent invention has an advantage in that virtual network mapping whichis not applied to a network size may be performed by embeddingcalculation and mapping per node.

A graph embedding-based virtual network mapping method according to thepresent invention has an advantage in that when the mapping per node isperformed, the scenario for the virtual network mapping may be optimizedby checking an allocable capacity of the network, and determiningembedding recalculation for the substrate network.

The effects of the present invention are not limited to theaforementioned effects, and various other effects may be included withina range which is obvious to those skilled in the art from the followingdescription.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a flowchart illustrating a graph embedding-based virtualnetwork mapping method according to the present invention.

FIG. 2 is an exemplary view illustrating a graph embedding-based virtualnetwork mapping structure according to the present invention.

FIGS. 3A to 3D are exemplary views of a performance to which a graphembedding-based virtual network mapping method according to the presentinvention is applied.

FIGS. 4A to 4D are exemplary views of a performance to which a graphembedding-based virtual network mapping method according to the presentinvention is applied.

FIGS. 5A and 5B are exemplary views of a performance to which a graphembedding-based virtual network mapping method according to the presentinvention is applied.

DETAILED DESCRIPTION FOR IMPLEMENTING INVENTION

Those skilled in the art may make various modifications to the presentinvention and the present invention may have various embodimentsthereof, and thus specific embodiments will be described in detail withreference to the drawings. However, this does not limit the presentinvention within specific exemplary embodiments, and it should beunderstood that the present invention covers all the modifications,equivalents and replacements within the spirit and technical scope ofthe present invention. In the description of respective drawings,similar reference numerals designate similar elements.

Terms such as first, second, A, or B may be used to describe variouscomponents but the components are not limited by the above terms. Theabove terms are used only to distinguish one component from the othercomponent. For example, without departing from the scope of the presentinvention, a first component may be referred to as a second component,and similarly, a second component may be referred to as a firstcomponent. A term of and/or includes combination of a plurality ofrelated elements or any one of the plurality of related elements.

It should be understood that, when it is described that an element is“coupled” or “connected” to another element, the element may be directlycoupled or directly connected to the other element or coupled orconnected to the other element through a third element. In contrast,when it is described that an element is “directly coupled” or “directlyconnected” to another element, it should be understood that no elementis not present therebetween.

Terms used in the present application are used only to describe aspecific exemplary embodiment, but are not intended to limit the presentinvention. A singular form may include a plural form if there is noclearly opposite meaning in the context. In the present application, itshould be understood that the term “include” or “have” indicates that afeature, a number, a step, an operation, a component, a part or thecombination thereof described in the specification is present, but donot exclude a possibility of presence or addition of one or more otherfeatures, numbers, steps, operations, components, parts or combinations,in advance.

If it is not contrarily defined, all terms used herein includingtechnological or scientific terms have the same meaning as thosegenerally understood by a person with ordinary skill in the art. Termsdefined in generally used dictionary shall be construed that they havemeanings matching those in the context of a related art, and shall notbe construed in ideal or excessively formal meanings unless they areclearly defined in the present application.

Throughout the specification and claims, when a part includes a certaincomponent, this means that it may further include other components, notexcluding other components unless otherwise stated.

Hereinafter, preferred embodiments according to the present inventionwill be described in detail with reference to the accompanying drawings.

FIG. 1 is a flowchart illustrating a graph embedding-based virtualnetwork mapping method according to the present invention and FIG. 2 isan exemplary view illustrating a graph embedding-based virtual networkmapping structure according to the present invention.

Referring to FIGS. 1 and 2 , a virtual network mapping method may inputa virtual network and a substrate network corresponding to a virtualnetwork mapping scenario in step S110.

In an optimized mapping mode, a network encoder may apply a graphconvolution network (GCN) to embed the virtual network to generate anembedding value for every substrate node in step S120.

A network decoder may determine whether a difference value between anembedding value for every substrate node and an embedding value forevery previous substrate node which is previously embedded exceeds a setthreshold in step S130.

When the difference value does not exceed the threshold value, thenetwork decoder may map an allocation node of the virtual network to amapping node of a previous substrate network which is previously mappedaccording to an embedding value for every previous substrate node instep S140.

In step S130, when the difference value exceeds the threshold value, thenetwork decoder may control the network encoder to allow the networkencoder to embed the substrate network to generate an embedding valuefor every node in step S150.

After the step S110, in a normal mapping mode, the network encoder mayembed the embedding value for every substrate node and the substratenetwork to generate an embedding value for every node in step S160.

The network decoder may map an allocation node of the virtual networkand a mapping node of the substrate network based on the embedding valuefor every substrate node and the embedding value for every node in stepS170.

According to the present invention, with a reinforcement learning basedencoder-decoder structure, the network encoder performs embedding of thevirtual network and the substrate network by iterative calculation pernode, and the network decoder may establish a policy of virtual networkmapping by iterative mapping per node using the embedding value.

In the policy process, both a normal mapping mode and an optimizedmapping mode are supported.

In the normal mapping mode, a virtual network allocation node and asubstrate network mapping node are determined at one time, and in theoptimized mapping mode, the virtual network allocation node isdetermined and then a mapping node of the substrate network may bedetermined thereafter.

In the optimized mapping mode, a capacity of the substrate network isrecalculated in real-time according to a discriminator to optimize bysetting the trade-off relationship between accuracy and speed.

As illustrated in FIG. 2 , the network encoder may commonly operate inboth the normal mapping mode and the optimized mapping mode.

Each node is denoted by v with respect to the virtual network V, andeach node may be expressed by s with respect to the substrate network S.

Here, the vector expressions are expressed by x^((v)) and x^((s))respectively, and the matrix expressions are expressed by X^((V)) andX^((S)). Further,

^((V)) expresses a node set of the virtual network, and

^((S)) expresses a node set of the substrate network.

In the embedding step, embedding E: [e₁, e₂, . . . , e_(n)] for eachnetwork is obtained by the following graph embedding equation.

$E = {\sum\limits_{i = 1}^{T}{{{MLP}(X)} \times L^{i}}}$

Here, L is a Laplacian matrix and represents an adjacent matrix valuebetween nodes, and T is a distance between nodes to be considered forembedding. That is, if T is 3, graph embedding NetEmbed(⋅) is performedin consideration of characteristics of three adjacent nodes during theembedding.

E^((V)) is a virtual network embedding value, and E^((S)) is a substratenetwork embedding value. These embedding values are time-variant.

First, in the normal mapping mode, the network decoder may operate asfollows.

E ^((V)) :[e ₁ ^((v)) , . . . ,e _(n) ^((v)) ],E ^((S)) :[e ₁ ^((s)) , .. . ,e _(m) ^((s))]

Each embedding value is formed of an embedding vector of nodes. Scoresare obtained as follows by paring nodes for the virtual network and thesubstrate networks using a trainable MLP function.

q ^((v,s)) =MLP([e ^((v)) :e ^((s))])

At this time, it is assumed that resources of the substrate network areallocated whenever nodes of the substrate network and the virtualnetwork are paired, so that the pairing is performed only when aresource which is required by a virtual network remains in the substratenetwork. That is, the pairing may be performed when the followingcondition is satisfied.

${{Alocable}\left( {v,s} \right)} = {\underset{c \in \mathcal{C}}{\land}{x_{c}^{(s)} \geq x_{c}^{(v)}}}$

Here, C refers to a set of sources, such as CPU or RAM, and x^((v)) _(c)and x^((s)) _(c) refer to resources of nodes of the virtual network andthe substrate network.

After obtaining scores for all possible combinations, rankings areassigned in an ascending order using the Softmax function and VPNmapping is performed on the pair having the highest score.

${\Pr\left\lbrack \left( {v,s} \right) \right\rbrack} = \frac{\exp\left( q^{({v,s})} \right)}{{\sum}_{{v^{\prime} \in \mathcal{O}_{k}^{(V)}},{v \in S}}{\exp\left( q^{({v,s})} \right)}}$

Here,

_(K) ^((V)) refers to a node set of the virtual network which is notallocated to the substrate network.

After performing the VNM mapping, a resource situation of the substratenetwork is changed so that the resource situation is updated by thefollowing equation and the substrate network graph embedding iscalculated again.

x _(c,i) ^((s)) =x _(c,i-1) ^((s)) −x _(c) ^((v))

In the optimized mapping mode, the network decoder may operate asfollows.

In the normal mapping mode, all the possible node pairs for the virtualnetwork and the substrate network are obtained and then a score for thepair is obtained. However, in the optimized mode, a node of the virtualnetwork is selected by the two-step mapping, and a node of the substratenetwork is selected based on the selected virtual network so that moreaccurate virtual network mapping is performed.

First, a score is calculated for the virtual network nodes as follows bythe MLP.

$q^{(v)} = {{{{MLP}\left( \left\lbrack {e^{(v)}:g^{(S)}:\frac{X_{\mathcal{C}}^{(S)}}{X_{\mathcal{C},\tau}^{(S)}}} \right\rbrack \right)}{for}v} \in \mathcal{O}_{k}^{(V)}}$

Here, X^((S)) _(c) is a total resource of the substrate network, X^((S))_(C,τ) is an available resource of the substrate network, and g^((S)) isa graph embedding value in the entire network level, and are calculatedas follows.

$g^{(S)} = {{MLP}\left( \left\lbrack {\sum\limits_{s \in \mathcal{O}_{k}^{(S)}}{e^{(s)}:\frac{❘\mathcal{O}_{k}^{(S)}❘}{❘\mathcal{O}^{(S)}❘}}} \right\rbrack \right)}$

Further,

^((S)) is a node set of the substrate network, and

_(K) ^((S)) is a set of nodes which have never been mapped, among nodesof the substrate network.

The score for the virtual network node is ranked by a value through thefollowing equation Softmax to select a node having a largest value.

${\Pr\lbrack v\rbrack} = \frac{\exp\left( q^{(v)} \right)}{{\sum}_{v^{\prime} \in \mathcal{O}_{k}^{(V)}}{\exp\left( q^{(v)} \right)}}$

When the node of the virtual network is selected, the score iscalculated for the nodes of the substrate network by the followingequation, based on the selected node.

$q^{(s)} = {{MLP}\left( \left\lbrack {e^{(v)}:e^{(s)}:\frac{\oplus_{c \in \mathcal{C}}x_{c}^{(s)}}{\oplus_{c \in \mathcal{C}}X_{c,\tau}^{(s)}}} \right\rbrack \right)}$

Here, ⊕ is a concatenation calculation.

Similar to the node selection of the virtual network, the ranking isobtained by the value through the following equation Softmax to select anode having the largest value to map the virtual network node and thesubstrate network node.

Similar to the normal mode, the process is repeated until the virtualnetwork can be mapped to the substrate network. In the optimized mode,it is determined whether to perform the network graph embedding throughthe following equation EMModel function rather than newly performing thesubstrate network embedding (a principle is to perform graph embeddingof the substrate network again because when the node of the virtualnetwork is mapped to the node of the substrate network, an availableresource situation for the substrate network is changed) after everynode pair mapping.

$\begin{matrix}{{dst} = {{EMModel}\left( {E_{\tau_{1}}^{(S)},{X_{\tau_{1}}^{{(S)},}X_{\tau_{2}}^{(S)}}} \right)}} \\{= {{MLP}\left( \left\lbrack {\sum\limits_{s \in \mathcal{O}_{\tau_{1}}^{(S)}}{e_{\tau_{1}}^{(s)}:\frac{X_{\mathcal{C},\tau_{2}}^{(S)}}{X_{\mathcal{C},\tau_{1}}^{(S)}}:\frac{❘\mathcal{O}_{\tau_{2}}^{(S)}❘}{❘\mathcal{O}_{\tau_{1}}^{(S)}❘}}} \right\rbrack \right)}}\end{matrix}$

The EMModel function predicts a difference for substrate networkembedding E_(τ) ₁ ^((S)) and E_(τ) ₂ ^((S)) for different times τ1 andτ2 so that if a value derived by the above equation exceeds a threshold,the substrate network graph embedding is performed, and if not, theexisting value is used as it is without performing the substrate networkgraph embedding.

EMModel may be trained by supervised learning by collecting E_(τ) ₁^((S)), X_(τ) ₁ ^((S)), X_(τ) ₂ ^((S)), |E_(τ) ₁ ^((S))−E_(τ) ₂ ^((S))|samples, differently from the MLP functions of the encoder-decoder whichare trained end to end by reinforcement learning.

FIGS. 3A to 3D are exemplary views of a performance to which a graphembedding-based virtual network mapping method according to the presentinvention is applied.

FIGS. 4A to 4D are exemplary views of a performance to which a graphembedding-based virtual network mapping method according to the presentinvention is applied.

FIGS. 5A and 5B are exemplary views of a performance to which a graphembedding-based virtual network mapping method according to the presentinvention is applied.

In FIGS. 3A to 3D, as an executing example of the present invention(Gemma), a virtual network pharming scenario of a data center isassumed. Slowdown refers to an actual executing time including aplacement time compared to an expected virtual network execution time,and the lower the slowdown, the better the performance.

In FIGS. 4A to 4D, as an executing example of the present invention(Gemma), an internet service provider VNM scenario is assumed. Theacceptance ratio refers to a number of virtual networks allocated to thesubstrate network with respect to the entire virtual network, and thehigher the acceptance ratio, the better the performance.

FIGS. 5A and 5B relate to the optimization of the present invention asan executing example of the present invention (Gemma). Gemma is anoptimized algorithm and one or two is excluded from the Gemma algorithmto represent the trade-off of the optimization.

The features, structures, effects and the like described in theforegoing embodiments are included in at least one embodiment of thepresent invention and are not necessarily limited to one embodiment.Moreover, the features, structures, effects and the like illustrated ineach embodiment may be combined or modified by those skilled in the artfor the other embodiments to be carried out. Therefore, the combinationand the modification of the present invention are interpreted to beincluded within the scope of the present invention.

It will be appreciated that various exemplary embodiments of the presentinvention have been described herein for purposes of illustration, andthat various modifications, changes, and substitutions may be made bythose skilled in the art without departing from the scope and spirit ofthe present invention. Therefore, the exemplary embodiments of thepresent invention are provided for illustrative purposes only but notintended to limit the technical concept of the present invention. Thescope of the technical concept of the present invention is not limitedthereto. The protective scope of the present invention should beconstrued based on the following claims, and all the technical conceptsin the equivalent scope thereof should be construed as falling withinthe scope of the present invention.

1. A graph embedding-based virtual network mapping method, comprising:inputting a virtual network and a substrate network corresponding to avirtual network mapping scenario; in an optimized mapping mode,generating an embedding value for every substrate node by applying agraph convolution network (GCN) to embed the virtual network, by anetwork encoder; determining whether a difference value between anembedding value for every substrate node and an embedding value forevery previous substrate node which is previously embedded exceeds a setthreshold, by a network decoder; and mapping an allocation node of thevirtual network to a mapping node of a previous substrate network whichis previously mapped according to an embedding value for every previoussubstrate node when the difference value does not exceed the thresholdvalue, by the network decoder.
 2. The graph embedding-based virtualnetwork mapping method according to claim 1, further comprising:controlling the network encoder to generate an embedding value for everynode by embedding the substrate network by the network decoder when thedifference value exceeds the threshold.
 3. The graph embedding-basedvirtual network mapping method according to claim 2, wherein in themapping, an allocation node of the virtual network and a mapping node ofthe substrate network are mapped to each other based on the embeddingvalue for every substrate node and the embedding value for every node.4. The graph embedding-based virtual network mapping method according toclaim 3, wherein in the mapping, the allocation node having a highestembedding value is selected among the embedding values for everysubstrate node, and the allocation node and the mapping node are mapped.5. The graph embedding-based virtual network mapping method according toclaim 1, further comprising: generating an embedding value for everynode by embedding the embedding value for every substrate node and thesubstrate network by the network encoder, in a normal mapping mode; andmapping an allocation node of the virtual network and a mapping node ofthe substrate network based on the embedding value for every substratenode and the embedding value for every node, by the network decoder. 6.The graph embedding-based virtual network mapping method according toclaim 1, further comprising: determining whether a current mode is anoptimized mapping mode before the generating of the embedding value. 7.The graph embedding-based virtual network mapping method according toclaim 1, wherein in the generating of an embedding value, the embeddingvalue for every substrate node and the embedding value for every nodeare generated by applying a distance between nodes of the virtualnetwork and a distance between nodes of the substrate network to the MLPfunction.